A Multi-feature Approach for Noise Detection in Lung Sounds

During the acquisition of lung sounds, several sources of noise can interfere with the recordings. Therefore, the detection of noise present in lung sounds plays an important role in the correct diagnosis of several pulmonary disorders such as in chronic obstructive pulmonary diseases. Denoising tools reported so far focus mainly in the detection of abnormal lung sounds from the background noise (usually vesicular background) or even just in the discrimination of normal from abnormal lung sounds. Algorithms for heart sound cancellation have also been proposed. However, it can be noticed that there is a lack of signal processing methods to efficiently detected and/or remove artifacts introduced in the acquisition environment or produced by the subject (e.g., speech). The present study focuses in the analysis of lungs sounds recorded in two different populations containing events of cough, speech and other artifacts from the surrounding environment. Feature extraction and binary classification were performed achieving, on average, values of a sensitivity and specificity ranging from 76 to 97% for the classification of cough, speech and other artifacts and from 83 to 90% for the specific detection of cough events. The detection of artifacts achieved sensitivity and specificity values of 84% and 61%, respectively for one population and 88% and 52% for another population.